• Title, Summary, Keyword: hand gesture recognition

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Hand Gesture Recognition Using Shape Similarity Based On Feature Points Of Contour (윤곽선 특징점 기반 형태 유사도를 이용한 손동작 인식)

  • Yi, Hong-Ryoul;Choi, Chang;Kim, Pan-Koo
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.585-588
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    • 2008
  • This paper proposes hand gesture recognition using shape similarity method. For this, we require two steps which are aquisition of Hand area and similarity evaluation. First step is extracting hand area using YCbCr color spare. Then eliminate noise through filter and analyzing histogram. For doing this, we ran measure similarity of hand gesture by applying TSR after getting contour. Finally, we utilize shape similarity for recognizing of hand gesture.

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Vision-based hand gesture recognition system for object manipulation in virtual space (가상 공간에서의 객체 조작을 위한 비전 기반의 손동작 인식 시스템)

  • Park, Ho-Sik;Jung, Ha-Young;Ra, Sang-Dong;Bae, Cheol-Soo
    • Proceedings of the IEEK Conference
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    • pp.553-556
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    • 2005
  • We present a vision-based hand gesture recognition system for object manipulation in virtual space. Most conventional hand gesture recognition systems utilize a simpler method for hand detection such as background subtractions with assumed static observation conditions and those methods are not robust against camera motions, illumination changes, and so on. Therefore, we propose a statistical method to recognize and detect hand regions in images using geometrical structures. Also, Our hand tracking system employs multiple cameras to reduce occlusion problems and non-synchronous multiple observations enhance system scalability. Experimental results show the effectiveness of our method.

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A Dynamic Hand Gesture Recognition System Incorporating Orientation-based Linear Extrapolation Predictor and Velocity-assisted Longest Common Subsequence Algorithm

  • Yuan, Min;Yao, Heng;Qin, Chuan;Tian, Ying
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.9
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    • pp.4491-4509
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    • 2017
  • The present paper proposes a novel dynamic system for hand gesture recognition. The approach involved is comprised of three main steps: detection, tracking and recognition. First, the gesture contour captured by a 2D-camera is detected by combining the three-frame difference method and skin-color elliptic boundary model. Then, the trajectory of the hand gesture is extracted via a gesture-tracking algorithm based on an occlusion-direction oriented linear extrapolation predictor, where the gesture coordinate in next frame is predicted by the judgment of current occlusion direction. Finally, to overcome the interference of insignificant trajectory segments, the longest common subsequence (LCS) is employed with the aid of velocity information. Besides, to tackle the subgesture problem, i.e., some gestures may also be a part of others, the most probable gesture category is identified through comparison of the relative LCS length of each gesture, i.e., the proportion between the LCS length and the total length of each template, rather than the length of LCS for each gesture. The gesture dataset for system performance test contains digits ranged from 0 to 9, and experimental results demonstrate the robustness and effectiveness of the proposed approach.

Deep Learning Based 3D Gesture Recognition Using Spatio-Temporal Normalization (시 공간 정규화를 통한 딥 러닝 기반의 3D 제스처 인식)

  • Chae, Ji Hun;Gang, Su Myung;Kim, Hae Sung;Lee, Joon Jae
    • Journal of Korea Multimedia Society
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    • v.21 no.5
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    • pp.626-637
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    • 2018
  • Human exchanges information not only through words, but also through body gesture or hand gesture. And they can be used to build effective interfaces in mobile, virtual reality, and augmented reality. The past 2D gesture recognition research had information loss caused by projecting 3D information in 2D. Since the recognition of the gesture in 3D is higher than 2D space in terms of recognition range, the complexity of gesture recognition increases. In this paper, we proposed a real-time gesture recognition deep learning model and application in 3D space using deep learning technique. First, in order to recognize the gesture in the 3D space, the data collection is performed using the unity game engine to construct and acquire data. Second, input vector normalization for learning 3D gesture recognition model is processed based on deep learning. Thirdly, the SELU(Scaled Exponential Linear Unit) function is applied to the neural network's active function for faster learning and better recognition performance. The proposed system is expected to be applicable to various fields such as rehabilitation cares, game applications, and virtual reality.

Hand gesture recognition for player control

  • Shi, Lan Yan;Kim, Jin-Gyu;Yeom, Dong-Hae;Joo, Young-Hoon
    • Proceedings of the KIEE Conference
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    • pp.1908-1909
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    • 2011
  • Hand gesture recognition has been widely used in virtual reality and HCI (Human-Computer-Interaction) system, which is challenging and interesting subject in the vision based area. The existing approaches for vision-driven interactive user interfaces resort to technologies such as head tracking, face and facial expression recognition, eye tracking and gesture recognition. The purpose of this paper is to combine the finite state machine (FSM) and the gesture recognition method, in other to control Windows Media Player, such as: play/pause, next, pervious, and volume up/down.

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Hand gesture based a pet robot control (손 제스처 기반의 애완용 로봇 제어)

  • Park, Se-Hyun;Kim, Tae-Ui;Kwon, Kyung-Su
    • Journal of the Korea Industrial Information Systems Research
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    • v.13 no.4
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    • pp.145-154
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    • 2008
  • In this paper, we propose the pet robot control system using hand gesture recognition in image sequences acquired from a camera affixed to the pet robot. The proposed system consists of 4 steps; hand detection, feature extraction, gesture recognition and robot control. The hand region is first detected from the input images using the skin color model in HSI color space and connected component analysis. Next, the hand shape and motion features from the image sequences are extracted. Then we consider the hand shape for classification of meaning gestures. Thereafter the hand gesture is recognized by using HMMs (hidden markov models) which have the input as the quantized symbol sequence by the hand motion. Finally the pet robot is controlled by a order corresponding to the recognized hand gesture. We defined four commands of sit down, stand up, lie flat and shake hands for control of pet robot. And we show that user is able to control of pet robot through proposed system in the experiment.

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Human Gesture Recognition Technology Based on User Experience for Multimedia Contents Control (멀티미디어 콘텐츠 제어를 위한 사용자 경험 기반 동작 인식 기술)

  • Kim, Yun-Sik;Park, Sang-Yun;Ok, Soo-Yol;Lee, Suk-Hwan;Lee, Eung-Joo
    • Journal of Korea Multimedia Society
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    • v.15 no.10
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    • pp.1196-1204
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    • 2012
  • In this paper, a series of algorithms are proposed for controlling different kinds of multimedia contents and realizing interact between human and computer by using single input device. Human gesture recognition based on NUI is presented firstly in my paper. Since the image information we get it from camera is not sensitive for further processing, we transform it to YCbCr color space, and then morphological processing algorithm is used to delete unuseful noise. Boundary Energy and depth information is extracted for hand detection. After we receive the image of hand detection, PCA algorithm is used to recognize hand posture, difference image and moment method are used to detect hand centroid and extract trajectory of hand movement. 8 direction codes are defined for quantifying gesture trajectory, so the symbol value will be affirmed. Furthermore, HMM algorithm is used for hand gesture recognition based on the symbol value. According to series of methods we presented, we can control multimedia contents by using human gesture recognition. Through large numbers of experiments, the algorithms we presented have satisfying performance, hand detection rate is up to 94.25%, gesture recognition rate exceed 92.6%, hand posture recognition rate can achieve 85.86%, and face detection rate is up to 89.58%. According to these experiment results, we can control many kinds of multimedia contents on computer effectively, such as video player, MP3, e-book and so on.

Morphological Hand-Gesture Recognition Algorithm (형태론적 손짓 인식 알고리즘)

  • Choi Jong-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.8 no.8
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    • pp.1725-1731
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    • 2004
  • The use of gestures provides an attractive alternate to cumbersome interface devices for human-computer interaction. This has motivated a very active research area concerned with computer vision-based analysis and interpretation of hand gestures. The most important issues in gesture recognition are the simplification of algorithm and the reduction of processing time. The mathematical morphology based on geometrical set theory is best used to perform the processing. A key idea of proposed algorithm in this paper is to apply morphological shape decomposition. The primitive elements extracted to a hand gesture include in very important information on the directivity of the hand gestures. Based on this characteristic, we proposed the morphological gesture recognition algorithm using feature vectors calculated to lines connecting the center points of a main-primitive element and sub-primitive elements. Through the experiment, we demonstrated the efficiency of proposed algorithm. Coupling natural interactions such as hand gesture with an appropriately designed interface is a valuable and powerful component in the building of TV switch navigating and video contents browsing system.

Vision-based hand Gesture Detection and Tracking System (비전 기반의 손동작 검출 및 추적 시스템)

  • Park Ho-Sik;Bae Cheol-soo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.30 no.12C
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    • pp.1175-1180
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    • 2005
  • We present a vision-based hand gesture detection and tracking system. Most conventional hand gesture recognition systems utilize a simpler method for hand detection such as background subtractions with assumed static observation conditions and those methods are not robust against camera motions, illumination changes, and so on. Therefore, we propose a statistical method to recognize and detect hand regions in images using geometrical structures. Also, Our hand tracking system employs multiple cameras to reduce occlusion problems and non-synchronous multiple observations enhance system scalability. In this experiment, the proposed method has recognition rate of $99.28\%$ that shows more improved $3.91\%$ than the conventional appearance method.

A Novel Door Security System using Hand Gesture Recognition (손동작 인식을 이용한 출입 보안 시스템)

  • Cheoi, Kyungjoo;Han, Juchan
    • Journal of Korea Multimedia Society
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    • v.19 no.8
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    • pp.1320-1328
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    • 2016
  • In this paper, we propose a novel security system using hand gesture recognition. Proposed system does not create a password as numbers, but instead, it creates unique yet simple pattern created by user's hand movement. Because of the fact that individuals have different range of hand movement, speed, direction, and size while drawing a pattern with their hands, the system will be able to accurately recognize only the authorized user. To evaluate the performance of our system, various patterns were tested and the test showed a satisfying result.